



'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :						List[Any]	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Dict	=  {
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}

class 							a__( snake_case__     ):
			a_	: Any									=      '''trocr'''
			a_	: List[str]									=      ['''past_key_values''']
			a_	: List[str]									=      {
			    '''num_attention_heads''': '''decoder_attention_heads''',
			    '''hidden_size''': '''d_model''',
			    '''num_hidden_layers''': '''decoder_layers''',
			}






			def __init__(     self					,   _UpperCAmelCase=5_0265					,   _UpperCAmelCase=1024					,   _UpperCAmelCase=12					,   _UpperCAmelCase=16					,   _UpperCAmelCase=4096					,   _UpperCAmelCase="gelu"					,   _UpperCAmelCase=512					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.0					,   _UpperCAmelCase=0.0					,   _UpperCAmelCase=2					,   _UpperCAmelCase=0.02					,   _UpperCAmelCase=0.0					,   _UpperCAmelCase=True					,   _UpperCAmelCase=False					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=1					,   _UpperCAmelCase=0					,   _UpperCAmelCase=2					,   **_UpperCAmelCase					,   )							->							str:
					snake_case__	     =vocab_size
					snake_case__	     =d_model
					snake_case__	     =decoder_layers
					snake_case__	     =decoder_attention_heads
					snake_case__	     =decoder_ffn_dim
					snake_case__	     =activation_function
					snake_case__	     =max_position_embeddings
					snake_case__	     =dropout
					snake_case__	     =attention_dropout
					snake_case__	     =activation_dropout
					snake_case__	     =init_std
					snake_case__	     =decoder_layerdrop
					snake_case__	     =use_cache
					snake_case__	     =scale_embedding
					snake_case__	     =use_learned_position_embeddings
					snake_case__	     =layernorm_embedding

					super().__init__(
					    pad_token_id=_UpperCAmelCase					,   bos_token_id=_UpperCAmelCase					,   eos_token_id=_UpperCAmelCase					,   decoder_start_token_id=_UpperCAmelCase					,   **_UpperCAmelCase					,   )









'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    int			)       ->	tuple[float, list[float]]:
		snake_case__	     =list(range(len(UpperCamelCase_			)			)			)
		snake_case__	     =[v / w for v, w in zip(UpperCamelCase_      ,					UpperCamelCase_			)]
		index.sort(key=lambda UpperCamelCase_			: ratio[i]      ,					reverse=UpperCamelCase_			)

		snake_case__	     =0
		snake_case__	     =[0] * len(UpperCamelCase_			)
		for i in index:
				if weight[i] <= capacity:
						snake_case__	     =1
						max_value += value[i]
						capacity -= weight[i]
				else:
						snake_case__	     =capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()




